Mechanical properties and durability of concrete with zeolite and waste ceramic powder through experimental investigation and machine learning analysis

This study investigates the combined influence of zeolite and ceramic powder as supplementary cementitious materials on the mechanical and durability performance of concrete and develops machine-learning models to accurately predict these properties. Although previous studies have evaluated these materials individually, no prior work has systematically examined their synergistic effects across multiple replacement levels or integrated experimental findings with advanced predictive modeling. A comprehensive experimental program was conducted to assess the compressive strength and chloride penetration resistance of hybrid mixtures. Results show that the optimal combination (15% zeolite and 30% ceramic powder) significantly enhanced durability, reducing the RCPT charge from 3235.1 to 425.7 coulombs w 86.8% reduction, statistically significant (p < 0.05). To extend the applicability of the findings beyond the tested mix designs, several machine-learning algorithms were trained using the experimental dataset, with XGBoost demonstrating the highest predictive accuracy (RMSE = 1.5, R2 = 0.91). These models provide reliable estimations of concrete performance and reduce the need for extensive laboratory testing. Overall, the results highlight that the synergistic use of zeolite and ceramic powder not only improves mechanical and durability performance but also reduces cement demand and recycles industrial waste, offering a practical and environmentally sustainable approach for producing more durable concrete.